International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 131, P. 136 - 144
Published: April 27, 2025
Language: Английский
International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 131, P. 136 - 144
Published: April 27, 2025
Language: Английский
International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 120, P. 486 - 496
Published: March 29, 2025
Language: Английский
Citations
3Energy Strategy Reviews, Journal Year: 2025, Volume and Issue: 58, P. 101661 - 101661
Published: Feb. 27, 2025
Language: Английский
Citations
0Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 179730 - 179730
Published: March 1, 2025
Language: Английский
Citations
0Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 72, P. 107501 - 107501
Published: March 17, 2025
Language: Английский
Citations
0International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 118, P. 426 - 440
Published: March 20, 2025
Language: Английский
Citations
0Biological Trace Element Research, Journal Year: 2025, Volume and Issue: unknown
Published: March 26, 2025
Language: Английский
Citations
0Diamond and Related Materials, Journal Year: 2025, Volume and Issue: unknown, P. 112257 - 112257
Published: March 1, 2025
Language: Английский
Citations
0Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13
Published: April 3, 2025
Municipal Solid Waste Generation (MSWG) presents a significant challenge for sustainable urban development, with waste production escalating at alarming rates worldwide. To address this issue, accurate predictive models are essential optimizing management strategies. This study utilizes comprehensive dataset of 4,343 records from municipal management, incorporating variables such as population density, urbanization indices, and composition. Advanced machine learning algorithms, including Decision Trees (DT), Random Forest (RF), LightGBM, XGBoost, employed, XGBoost being introduced novel approach MSWG prediction. Its ability to model complex nonlinear relationships, handle missing data outliers robustly, prevent overfitting through advanced regularization techniques sets it apart other models. The finds that outperforms the achieving an R 2 value 0.985 RMSE 0.056, making most predictor MSWG. flexibility scalability further enhance its applicability in managing diverse datasets, feature-ranking capability is instrumental identifying key factors influencing generation. results demonstrate into frameworks can significantly improve resource allocation, reduce operational costs, contribute environmental sustainability. not only advances methodologies but also provides actionable insights planners policymakers effectively tackling growing crisis. findings highlight potential learning, particularly transformative tool strategic decision-making management.
Language: Английский
Citations
0Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 72, P. 107632 - 107632
Published: April 1, 2025
Language: Английский
Citations
0Energy Strategy Reviews, Journal Year: 2025, Volume and Issue: 59, P. 101696 - 101696
Published: April 7, 2025
Language: Английский
Citations
0